def __init__(self,
access_config,
controller_config,
output_size,
name='components'):
"""Initializes the DNC core.
Args:
access_config: dictionary of access module configurations.
controller_config: dictionary of controller (LSTM) module configurations.
output_size: output dimension size of core.
clip_value: clips controller and core output values to between
`[-clip_value, clip_value]` if specified.
name: module name (default 'dnc').
Raises:
TypeError: if direct_input_size is not None for any access module other
than KeyValueMemory.
"""
super(Components, self).__init__(name=name)
with self._enter_variable_scope():
self.controller = snt.DeepRNN(
[snt.LSTM(**controller_config),
snt.LSTM(**controller_config)])
#self.controller = snt.LSTM(**controller_config)
self.access = access.MemoryAccess(**access_config)
self.output_linear = snt.Linear(output_size=output_size,
use_bias=False)
if FLAGS.is_input_embedder:
self.input_embedder = snt.Sequential(
[snt.Linear(output_size=64, use_bias=True), tf.nn.tanh])
def __init__(self,
access_config,
controller_config,
output_size,
clip_value=None,
name='dnc'):
"""Initializes the DNC core.
Args:
access_config: dictionary of access module configurations.
controller_config: dictionary of controller (LSTM) module configurations.
output_size: output dimension size of core.
clip_value: clips controller and core output values to between
`[-clip_value, clip_value]` if specified.
name: module name (default 'dnc').
Raises:
TypeError: if direct_input_size is not None for any access module other
than KeyValueMemory.
"""
super(DNC, self).__init__(name=name)
with self._enter_variable_scope():
self._controller = snt.LSTM(**controller_config)
self._access = access.MemoryAccess(**access_config)
self._access_output_size = np.prod(self._access.output_size.as_list())
self._output_size = output_size
self._clip_value = clip_value or 0
self._output_size = tf.TensorShape([output_size])
self._state_size = DNCState(
access_output=self._access_output_size,
access_state=self._access.state_size,
controller_state=self._controller.state_size)
def __init__(self,
access_config,
controller_config,
output_size,
name='components'):
"""Initializes the DNC core.
Args:
access_config: dictionary of access module configurations.
controller_config: dictionary of controller (LSTM) module configurations.
output_size: output dimension size of core.
clip_value: clips controller and core output values to between
`[-clip_value, clip_value]` if specified.
name: module name (default 'dnc').
Raises:
TypeError: if direct_input_size is not None for any access module other
than KeyValueMemory.
"""
super(Components, self).__init__(name=name)
with self._enter_variable_scope():
self.controller = snt.DeepRNN([
snt.LSTM(**controller_config) for _ in range(FLAGS.lstm_depth)
],
skip_connections=True)
#self.controller = snt.LSTM(**controller_config)
self.access = access.MemoryAccess(**access_config)
self.output_linear = snt.Linear(output_size=output_size,
use_bias=False)
if FLAGS.is_input_embedder:
self.input_embedder = snt.Sequential(
[snt.Linear(output_size=64, use_bias=True), tf.nn.tanh])
if FLAGS.is_variable_initial_states:
def c_fn(x):
shape = x.get_shape().as_list()
y = tf.Variable(initial_value=tf.zeros(shape=[1] +
shape[1:]),
dtype=tf.float32,
trainable=True)
return y
self.initial_controller_state = tf.contrib.framework.nest.map_structure(
c_fn, self.controller.initial_state(1, tf.float32))
def setUp(self):
self.module = access.MemoryAccess(MEMORY_SIZE, WORD_SIZE, NUM_READS,
NUM_WRITES)
self.initial_state = self.module.initial_state(BATCH_SIZE)
